In digital data recovery schemes, it is desirable to be able to provide an on-chip method for estimating the quality of recovered data which is independent of data patterns. The quality information can then provide a figure of merit for enabling key system parameters to be optimized. The measure of channel data quality is the average bit-error-rate (BER) and in some conventional schemes has been achieved by measuring the frequency of amplitude deviation from a specified reference level.
For example, U.S. Pat. No. 4,234,954 discloses an on-line circuit configured to estimate the bit error rate (BER) of a binary data signal stream in the presence of noise uncorrelated with the signal. For a binary data signal having two states (i.e., plus V and minus V) biased around a specified reference level (REF), the circuit counts the number of instances in which the received signal deviates more than 2V from the reference level REF. The accumulated count provides an accurate estimate of the BER over several orders of magnitude variation of the BER.
U.S. Pat. No. 3,721,959 discloses a method and means of error rate detection. The scheme (i) develops an eye pattern analog signal of transmitted digital data, defining a region within the eye pattern as an unacceptable area through which the eye pattern may not transgress and (ii) counts, as an erroneous signal, each transgression of the analog signal into the region. The drawback of such conventional schemes is the error rate estimation. The error rate estimation is not reflective of errors induced by clock jitter.
Another conventional method for estimating the channel BER can be obtained by measuring the statistics of the phase errors (i.e., the time between a clock edge and a data edge). Such a method takes into account errors induced by both noise on the input data signal and clock jitter. As shown in FIG. 1, in an ideal system the clock signal 1 and the data signal 2 should have edges 3, 4 which are co-incident. However, due to channel imperfections, the position of the clock edge 5 relative to the data edge 6 will vary as shown in FIG. 2. Furthermore, the average error may not have a mean of zero. A data error will occur if the time, ΔT (or phase error), between the clock edge 5 and the data edges 6 is >½ or <−{fraction (1/2)}, where ΔT has been normalized to the clock period T (i.e., if the data falls in the wrong clock window). Thus, the phase error statistics enable an estimate of the average channel performance to be predicted. For example, though the phase noise probability density function (pdf) is not a simple function, it will typically be Gaussian. Therefore, to evaluate the probability of error in a system requires a complex integral to be evaluated.